In computed tomography, there are a range of different reconstruction algorithms for computing cross-sectional images (also known as “slices”) from projection data of an object gathered during measurement at a detector of a CT scanner. There are reconstruction algorithms that iteratively build up a final image from an initial image. Some iterative reconstruction algorithms use regularization. The regularized “reconstruction problem”, that is, “Given a constraint on image property, how does one get from the initial image to the final image?”, is commonly formulated in terms of minimizing a cost function consisting of a data term and a regularization term. A further algorithmic variant is regularized statistical iterative reconstruction, where the data term accounts for a statistical model of the noise of the underlying measurements while the regularization term incorporates a-priory knowledge about the image to reconstruct. WO 2013088294 A1 describes such a statistical iterative reconstruction algorithm. It has been observed however that statistical model and regularization may lead to certain image properties like local resolution or SNR (signal-to-noise-ratio) to vary over the image in an undesirable way. A number of approaches have been proposed to enforce for instance uniformity of resolution such as J A Fessler et al in “Spatial Resolution Properties of Penalized-Likelihood Image Reconstruction: Space-Invariant Tomographs”, IEEE Transactions on Image Processing, 1996, 5, 1346-1358.